---
title: "Coronavirus en México"
author: "Eniak"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
suppressMessages(library(flexdashboard))
suppressMessages(library(rvest))
suppressMessages(library(readr))
suppressMessages(library(lubridate))
suppressMessages(library(stringr))
suppressMessages(library(plotly))
suppressMessages(library(dplyr))
suppressMessages(library(rgdal))
suppressMessages(library(leaflet))
suppressMessages(library(RColorBrewer))
suppressMessages(library(DT))

source("scripts/scraping.R", encoding = "utf-8")

#------------------ Parameters ------------------

confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"

#------------------ Data ------------------
df_it <- getTablaItalia()

df_es <- getTablaEspana()

df_mx <- getTablaHistorico() %>%
  mutate(Pais = "Mexico") %>%
  filter(confirmed > 0) %>%
  mutate(lag_confirmed = lag(confirmed, default = 0),
         lag_deaths = lag(deaths, default = 0),
         new_cases = confirmed - lag_confirmed,
         new_deaths = deaths - lag_deaths,
         days_since_first_case = row_number()) %>%
  select(Pais, date, confirmed, days_since_first_case, deaths, new_cases, new_deaths)

df_estados_mx <- getTablaEstados()


```

Acumulados
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df_mx$new_cases), big.mark = ","), "", sep = " "),
  caption = "Casos confirmados totales",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df_mx$new_deaths, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df_mx$new_deaths, na.rm = TRUE) / sum(df_mx$new_cases), 1),
    "%)",
    sep = ""
  ),
  caption = "Muertes (%)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Casos diarios acumulados (México)**
    
```{r}
plotly::plot_ly(data = df_mx) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmados",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~deaths,
    type = "scatter",
    mode = "lines+markers",
    name = "Muertes",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-28"),
    y = 1,
    text = paste("Primer caso"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-20"),
    y = 3,
    text = paste("Primera muerte"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-24"),
    y = 14,
    text = paste("Fase 2"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número de casos acumulados"),
    xaxis = list(title = "Fecha"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

### **Casos diarios acumulados (curva ajustada)**
```{r}

fit <- lm(log(confirmed) ~ date, data = df_mx)
exp_trend <- exp(predict(fit, data.frame(date=df_mx$date), interval = "prediction"))
df_1 <- cbind(df_mx, exp_trend)

plotly::plot_ly(data = df_1,
                x = ~date,
                y = ~upr,
                type = 'scatter',
                mode = 'lines',
                line = list(color = 'transparent'),
                name = 'Intervalo de predicción 95%',
                showlegend = FALSE
                ) %>%
  plotly::add_trace(y = ~lwr,
                    type = 'scatter',
                    mode = 'lines',
                    fill = 'tonexty',
                    fillcolor='rgba(0,100,80,0.2)',
                    line = list(color = 'transparent'),
                    showlegend = TRUE,
                    name = 'Intervalo de predicción 95%') %>%
    plotly::add_trace(y = ~fit,
                    type = 'scatter',
                    mode = 'lines',
                    line = list(color = 'rgba(0,100,80)', dash = 'dot'),
                    showlegend = TRUE,
                    name = 'modelo') %>%
  plotly::add_trace(x = ~date,
                     y = ~confirmed,
                     type = 'scatter',
                     mode = 'lines+markers',
                    showlegend = TRUE,
                     line = list(color=active_color),
                     marker = list(color = active_color),
                     name = 'Confirmados') %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Número de casos acumulados"),
                 xaxis = list(title = "Fecha"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare"
  )



```



Diarios
=======================================================================


row {data-width=650}
-----------------------------------------------------------------------
### **Evolución de los casos nuevos en México**
```{r}

#----------------------------------------
# Plotting the data

df_mx %>%
  filter(new_cases != 0) %>%
  select(date, new_cases) %>%
  #accumulate_by(~date) %>%
  plotly::plot_ly(
    x = ~date,
    y = ~new_cases,
    #frame = ~frame,
    mode = 'lines',
    fill = 'tozeroy',
    type = 'scatter',
    name = "Mexico",
    line = list(simplyfy = F)
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número de casos nuevos diarios",autoscale =T, range = c(0, max(df_mx$new_cases))),
    xaxis = list(title = "Fecha", autoscale =T, range = c(min(df_mx$date), max(df_mx$date))),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare")
  # ) %>%
  # animation_opts(
  #   frame = 100,
  #   transition = 0,
  #   redraw = FALSE
  # ) %>%
  # 
  # animation_button(
  #   x = 1,
  #   xanchor = 'right',
  #   y = 0,
  #   yanchor = 'bottom'
  # )

```

Row 
-----------------------------------------------------------------------


### **Casos nuevos por día en México y otros paises**
```{r}
df_1 <- rbind(df_mx, df_es) %>%
  rbind(df_it) 

df_it_new <- df_it %>%
  mutate(new_cases = new_cases + 1)
fit_it <- lm(log(new_cases) ~ days_since_first_case, data = df_it_new)
exp_trend_it <- exp(predict(fit_it, data.frame(days_since_first_case=df_it$days_since_first_case), interval = "prediction"))  %>%
  as_tibble() %>%
  mutate(Pais = "Italia",
         days_since_first_case=df_it_new$days_since_first_case)

df_mx_new <- df_mx %>%
  mutate(new_cases = new_cases + 1)
fit_mx <- lm(log(new_cases) ~ (days_since_first_case), data = df_mx_new)
exp_trend_mx <- exp(predict(fit_mx, data.frame(days_since_first_case=df_mx_new$days_since_first_case), interval = "prediction")) %>%
  as_tibble() %>%
  mutate(Pais = "Mexico",
         days_since_first_case=df_mx_new$days_since_first_case)

df_es_new <- df_es %>%
  mutate(new_cases = new_cases + 1)
fit_es <- lm(log(new_cases) ~ days_since_first_case, data = df_es_new)
exp_trend_es <- exp(predict(fit_es, data.frame(days_since_first_case=df_es_new$days_since_first_case), interval = "prediction")) %>%
  as_tibble() %>%
  mutate(Pais = "España",
         days_since_first_case=df_es_new$days_since_first_case)

df_trends <- rbind(exp_trend_mx, exp_trend_es) %>%
  rbind(exp_trend_it)

df_1 %>%
plotly::plot_ly(x = ~days_since_first_case,
                y = ~new_cases,
                group = ~Pais,
                type = "scatter",
                color = ~Pais,
                mode = "lines+markers",
                text = ~paste('Fecha: ', date)) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número de casos nuevos diarios",autoscale =T, range = c(0, max(df_1$new_cases))),
    xaxis = list(title = "Días desde el primer reporte", autoscale =T),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  ) %>%
  plotly::add_trace(x = ~df_trends$days_since_first_case,
                    y = ~df_trends$fit,
                    group = "Pais",
                    type = "scatter",
                    mode = "lines",
                    line = list(dash = 'dot'),
                    name = 'modelo')
```




Comparación con otros países
=======================================================================
Column {data-width=400}
-------------------------------------

### **Distribución de casos por tipo**

```{r daily_summary}
daily_confirmed <- df_mx %>%
  rbind(df_it) %>%
  rbind(df_es)

plotly::plot_ly(
  data = daily_confirmed,
  x = ~Pais,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confirmed",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~deaths,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Mapa
=======================================================================

Row
------------------------------------------------------
### **Evolución de casos por estado**
```{r}
df_estados_mx %>%
  filter(Estados!="Totales") %>%
  group_by(Estados) %>%
  plot_ly(x = ~date,
          y = ~ Casos,
          group = ~Estados,
          color = ~Estados,
          type = "scatter",
          mode = "lines+markers")

```

### **Tabla de número de casos por estado**

```{r}
demograficos_mx <- readRDS("Data/demograficos_estados_mx.rds")

df_estados_mx %>%
  group_by(Estados) %>%

  mutate(Casos = ifelse(is.na(Casos), 0, Casos),
         Muertes = ifelse(is.na(Muertes), 0, Muertes),
         Casos_Nuevos = Casos - nth(Casos, -2)) %>%
  filter(Estados!="Totales") %>%
  filter(date == max(date)) %>%
  ungroup() %>%
  arrange(Estados) %>%
  left_join(demograficos_mx, by = c("Estados" = "Estado")) %>%
  mutate(Prevalencia = signif((Casos / Poblacion_2015_edo) * 100, digits = 2),
         Incidencia = signif((Casos_Nuevos / Poblacion_2015_edo) * 100, 2)) %>%
  select(Estados, Casos, Casos_Nuevos, Prevalencia, Incidencia, Muertes, Fecha = date) %>%
  datatable()
```


Row
--------------------------------
### **Reporte de casos por estado** (*use + and - icons to zoom in/out*)

```{r}
df_estados_cum <- df_estados_mx %>%
  group_by(Estados) %>%
  filter(date == max(date)) %>%
  mutate(Casos = ifelse(is.na(Casos), 0, Casos),
         Muertes = ifelse(is.na(Muertes), 0, Muertes)) %>%
  filter(Estados!="Totales") %>%
  ungroup() %>%
  arrange(Estados) %>%
  mutate(estados_order = estados_order) %>%
  arrange(estados_order) %>%
  left_join(demograficos_mx, by = c("Estados" = "Estado")) %>%
  mutate(densidad_zm = Poblacion_zm / Superficie_zm,
         Incidencia_edo = (Casos / Poblacion_2015_edo / Superficie_edo),
         Incidencia_zm = (Casos / Poblacion_zm / Superficie_zm),
         prc_pob_infect = Casos / Superficie_zm)

mexico <- readOGR(dsn = "Data/shp_files/mexico", layer = "mexico", encoding = "UTF-8", verbose = F)
mexico$casos_acumulados<-df_estados_cum$Casos
mexico$defunciones <- df_estados_cum$Muertes

pal <- colorNumeric("RdYlGn", NULL, n = 32, reverse = T)

state_popup_casos <- paste0('
Estado: ', 
                      mexico$name, 
                      "
Casos acumulados: ", 
                      mexico$casos_acumulados,
                      "
Defunciones: ",
                       mexico$defunciones,"
")

leaflet(data = mexico) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(casos_acumulados), 
              fillOpacity = 0.8, 
              color = "#BDBDC3", 
              weight = 1, 
              popup = state_popup_casos)
```


### **Reporte de defunciones por estado** (*use + and - icons to zoom in/out*)
```{r}

state_popup_defunciones <- paste0('
Estado: ', 
                                  mexico$name, 
                                  "
Defunciones: ", 
                                  mexico$defunciones, "
")

leaflet(data = mexico) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(defunciones), 
              fillOpacity = 0.8, 
              color = "#BDBDC3", 
              weight = 1, 
              popup = state_popup_defunciones)
```



Referencias
================================
### **Referencias**
1. Pandemia de enfermedad por coronavirus de 2020 en México: https://es.wikipedia.org/wiki/Pandemia_de_enfermedad_por_coronavirus_de_2020_en_M%C3%A9xico

2. Secreataría de Salud: https://www.gob.mx/salud/documentos/informacion-internacional-y-nacional-sobre-nuevo-coronavirus-2019-ncov?idiom=es
 
3. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action